OpenCV

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 7 million. The library is used extensively in companies, research groups and by governmental bodies. ..


References in zbMATH (referenced in 90 articles )

Showing results 1 to 20 of 90.
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  1. Belkadi, M.; Guislain, L.; Sergent, A.; Podvin, B.; Chillà, F.; Salort, J.: Experimental and numerical shadowgraph in turbulent Rayleigh-Bénard convection with a rough boundary: investigation of plumes (2020)
  2. Freudenthaler, Gerhard; Meurer, Thomas: PDE-based multi-agent formation control using flatness and backstepping: analysis, design and robot experiments (2020)
  3. Ruman Gerst; Anna Medyukhina; Marc Thilo Figge: MISA++: A standardized interface for automated bioimage analysis (2020) not zbMATH
  4. Thompson, S., Dowrick, T., Xiao, G., Ramalhinho, J., Robu, M., Ahmad, M., Taylor, D., Clarkson, M.J.: SnappySonic: An Ultrasound Acquisition Replay Simulator (2020) not zbMATH
  5. Ainsworth, Mark; Tugluk, Ozan; Whitney, Ben; Klasky, Scott: Multilevel techniques for compression and reduction of scientific data-quantitative control of accuracy in derived quantities (2019)
  6. Brito, Darlan N.; Pádua, Flávio L. C.; Lopes, Aldo P. C.: Using geometric interval algebra modeling for improved three-dimensional camera calibration (2019)
  7. Davide Micieli, Triestino Minniti, Giuseppe Gorini: NeuTomPy toolbox, a Python package for tomographic data processing and reconstruction (2019) not zbMATH
  8. Demiröz, Barış Evrim; Altınel, İ. Kuban; Akarun, Lale: Rectangle blanket problem: binary integer linear programming formulation and solution algorithms (2019)
  9. Edgar Riba, Dmytro Mishkin, Daniel Ponsa, Ethan Rublee, Gary Bradski: Kornia: an Open Source Differentiable Computer Vision Library for PyTorch (2019) arXiv
  10. Jeong, Chiyoon; Yang, Hyun S.; Moon, KyeongDeok: A novel approach for detecting the horizon using a convolutional neural network and multi-scale edge detection (2019)
  11. Ronda, José I.; Valdés, Antonio: Geometrical analysis of polynomial lens distortion models (2019)
  12. Sebastian Lamprecht: Pyoints: A Python package for point cloud, voxel and raster processing (2019) not zbMATH
  13. Badías, Alberto; Alfaro, Icíar; González, David; Chinesta, Francisco; Cueto, Elías: Reduced order modeling for physically-based augmented reality (2018)
  14. Bergemann, Nico; Juel, Anne; Heil, Matthias: Viscous drops on a layer of the same fluid: from sinking, wedging and spreading to their long-time evolution (2018)
  15. Chekunov, Alekseyĭ Yu.: Implementation of the fast algorithm for geometrical coding of digital images with the use of CUDA architecture (2018)
  16. Jason Laura; Kelvin Rodriguez; Adam C. Paquette; Evin Dunn: AutoCNet: A Python library for sparse multi-image correspondence identification for planetary data (2018) not zbMATH
  17. Jiang, Hao; Robinson, Daniel P.; Vidal, René; You, Chong: A nonconvex formulation for low rank subspace clustering: algorithms and convergence analysis (2018)
  18. Lakatos, Dóra; Somfai, Ellák; Méhes, Elod; Czirók, András: Soluble VEGFR1 signaling guides vascular patterns into dense branching morphologies (2018)
  19. Lynch, Stephen: Dynamical systems with applications using Python (2018)
  20. Maxime Rousseau; Jean-Marc Retrouvey: pfla: A Python Package for Dental Facial Analysis using Computer Vision and Statistical Shape Analysis (2018) not zbMATH

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Further publications can be found at: http://opencv.org/publications.html